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--- |
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license: apache-2.0 |
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tags: |
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- summarization |
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- generated_from_trainer |
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metrics: |
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- rouge |
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model-index: |
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- name: mt5-small-text-sum-5 |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# mt5-small-text-sum-5 |
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This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 2.3673 |
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- Rouge1: 21.51 |
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- Rouge2: 6.94 |
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- Rougel: 20.94 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0001 |
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- train_batch_size: 11 |
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- eval_batch_size: 11 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 40 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | |
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|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:| |
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| 4.5176 | 1.77 | 500 | 2.6172 | 16.23 | 5.35 | 16.14 | |
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| 3.073 | 3.55 | 1000 | 2.4755 | 17.77 | 5.53 | 17.67 | |
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| 2.8478 | 5.32 | 1500 | 2.4330 | 18.56 | 5.28 | 18.32 | |
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| 2.7152 | 7.09 | 2000 | 2.4423 | 18.31 | 5.1 | 18.14 | |
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| 2.6003 | 8.87 | 2500 | 2.3905 | 19.46 | 5.52 | 19.17 | |
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| 2.5218 | 10.64 | 3000 | 2.3660 | 19.58 | 5.93 | 19.07 | |
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| 2.4172 | 12.41 | 3500 | 2.3595 | 19.89 | 6.42 | 19.5 | |
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| 2.3841 | 14.18 | 4000 | 2.3564 | 20.38 | 6.67 | 19.99 | |
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| 2.3049 | 15.96 | 4500 | 2.3730 | 20.21 | 6.41 | 19.79 | |
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| 2.2596 | 17.73 | 5000 | 2.3532 | 20.27 | 6.38 | 19.95 | |
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| 2.2155 | 19.5 | 5500 | 2.3539 | 19.6 | 6.41 | 19.24 | |
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| 2.1657 | 21.28 | 6000 | 2.3511 | 21.13 | 6.19 | 20.79 | |
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| 2.1343 | 23.05 | 6500 | 2.3378 | 20.59 | 6.45 | 20.18 | |
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| 2.1032 | 24.82 | 7000 | 2.3510 | 19.91 | 6.28 | 19.6 | |
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| 2.068 | 26.6 | 7500 | 2.3452 | 19.37 | 6.11 | 19.1 | |
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| 2.0438 | 28.37 | 8000 | 2.3513 | 20.86 | 6.43 | 20.49 | |
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| 2.0191 | 30.14 | 8500 | 2.3673 | 21.51 | 6.94 | 20.94 | |
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| 2.0085 | 31.91 | 9000 | 2.3519 | 20.65 | 6.61 | 20.2 | |
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| 1.9797 | 33.69 | 9500 | 2.3728 | 21.01 | 6.33 | 20.6 | |
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| 1.9808 | 35.46 | 10000 | 2.3663 | 21.22 | 6.48 | 20.82 | |
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| 1.9605 | 37.23 | 10500 | 2.3581 | 20.45 | 6.41 | 20.06 | |
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| 1.9599 | 39.01 | 11000 | 2.3608 | 21.07 | 6.57 | 20.6 | |
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### Framework versions |
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- Transformers 4.26.1 |
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- Pytorch 1.13.1+cu116 |
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- Datasets 2.10.1 |
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- Tokenizers 0.13.2 |
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